Sine saturation transform

ABSTRACT

A transform for determining a physiological measurement is disclosed. The transform determines a basis function index from a physiological signal obtained through a physiological sensor. A basis function waveform is generated based on basis function index. The basis function waveform is then used to determine an optimized basis function waveform. The optimized basis function waveform is used to calculate a physiological measurement.

PRIORITY CLAIM

The present application claims priority benefit under 35 U.S.C. § 120to, and is a continuation of U.S. patent application Ser. No.11/417,914, filed May 3, 2006, entitled Sine Saturation Transform,”which is a continuation of, U.S. patent application Ser. No. 11/048,232,filed Feb. 1, 2005, entitled Signal Component Processor,” which is acontinuation of U.S. patent application Ser. No. 10/184,032, filed Jun.26, 2002, entitled “Signal Component Processor,” now U.S. Pat. No.6,850,787, which claims priority benefit under 35 U.S.C. § 119(e) fromU.S. Provisional Application No. 60/302,438, filed Jun. 29, 2001,entitled “Signal Component Processor.” The present application alsoincorporates the foregoing disclosures herein by reference.

CROSS-REFERENCE TO RELATED APPLICATIONS

The following applications are currently pending: U.S. patentapplication Ser. No. 11/417,914, filed May 3, 2006, entitled SineSaturation Transform,” and U.S. patent application Ser. No. 11/048,232,filed Feb. 1, 2005, entitled Signal Component Processor.

BACKGROUND OF THE INVENTION

Early detection of low blood oxygen is critical in the medical field,for example in critical care and surgical applications, because aninsufficient supply of oxygen can result in brain damage and death in amatter of minutes. Pulse oximetry is a widely accepted noninvasiveprocedure for measuring the oxygen saturation level of arterial blood,an indicator of oxygen supply. A pulse oximeter typically provides anumerical readout of the patient's oxygen saturation and pulse rate. Apulse oximetry system consists of a sensor attached to a patient, amonitor, and a cable connecting the sensor and monitor. Conventionally,a pulse oximetry sensor has both red (RD) and infrared (IR)light-emitting diode (LED) emitters and a photodiode detector. The pulseoximeter measurements are based upon the absorption by arterial blood ofthe two wavelengths emitted by the sensor. The pulse oximeteralternately activates the RD and IR sensor emitters and reads theresulting RD and IR sensor signals, i.e. the current generated by thephotodiode in proportion to the detected RD and IR light intensity, inorder to derive an arterial oxygen saturation value, as is well-known inthe art. A pulse oximeter contains circuitry for controlling the sensor,processing the sensor signals and displaying the patient's oxygensaturation and pulse rate.

SUMMARY OF THE INVENTION

FIG. 1A illustrates a plethysmograph waveform 110, which is a display ofblood volume, shown along the ordinate 101, over time, shown along theabscissa 102. The shape of the plethysmograph waveform 110 is a functionof heart stroke volume, pressure gradient, arterial elasticity andperipheral resistance. Ideally, the waveform 110 displays a short, steepinflow phase 111 during ventricular systole followed by a typicallythree to four times longer outflow phase 112 during diastole. A dicroticnotch 116 is generally attributed to closure of the aortic valve at theend of ventricular systole.

FIG. 1B illustrates a corresponding RD or IR sensor signal s(t) 130,such as described above. The typical plethysmograph waveform 110 (FIG.1A), being a function of blood volume, also provides a light absorptionprofile. A pulse oximeter, however, does not directly detect lightabsorption and, hence, does not directly measure the plethysmographwaveform 110. However, IR or RD sensor signals are 180° out-of-phaseversions of the waveform 110. That is, peak detected intensity 134occurs at minimum absorption 114 and minimum detected intensity 138occurs at maximum absorption 118.

FIG. 1C illustrates the corresponding spectrum of s(t), which is adisplay of signal spectral magnitude |S(ω)|, shown along the ordinate105, versus frequency, shown along the abscissa 106. The plethysmographspectrum is depicted under both high signal quality 150 and low signalquality 160 conditions. Low signal quality can result when a pulseoximeter sensor signal is distorted by motion-artifact and noise. Signalprocessing technologies such as described in U.S. Pat. No. 5,632,272,assigned to the assignee of the present invention and incorporated byreference herein, allow pulse oximetry to function through patientmotion and other low signal quality conditions.

Ideally, plethysmograph energy is concentrated at the pulse ratefrequency 172 and associated harmonics 174, 176. Accordingly,motion-artifact and noise may be reduced and pulse oximetry measurementsimproved by filtering out sensor signal frequencies that are not relatedto the pulse rate. Under low signal quality conditions, however, thefrequency spectrum is corrupted and the pulse rate fundamental 152 andharmonics 154, 156 can be obscured or masked, resulting in errors in thecomputed pulse rate. In addition, a pulse rate, physiologically, isdynamic, potentially varying significantly between different measurementperiods. Hence, maximum plethysmograph energy may not correspond to thecomputed pulse rate except under high signal quality conditions andstable pulse rates. Further, an oxygen saturation value calculated froman optical density ratio, such as a normalized red over infrared ratio,at the pulse rate frequency can be sensitive to computed pulse rateerrors. In order to increase the robustness of oxygen saturationmeasurements, therefore, it is desirable to improve pulse rate basedmeasurements by identifying sensor signal components that correspond toan optimization, such as maximum signal energy.

One aspect of a signal component processor comprises a physiologicalsignal, a basis function index determined from the signal, a basisfunction waveform generated according to the index, a component derivedfrom the sensor signal and the waveform, and a physiological measurementresponsive to the component. In one embodiment, the component isresponsive to the inner product of the sensor signal and the waveform.In another embodiment, the index is a frequency and the waveform is asinusoid at the frequency. In that embodiment, the signal processor mayfurther comprise a pulse rate estimate derived from the signal whereinthe frequency is selected from a window including the pulse rateestimate. The physiological measurement may be an oxygen saturationvalue responsive to a magnitude of the component.

Another aspect of a signal component processor comprises a signal input,a basis function indicator derived from the signal input, a plurality ofbasis functions generated according to the indicator, a plurality ofcharacteristics of the signal input corresponding to the basis functionsand an optimization of the characteristics so as to identify at leastone of said basis functions. In one embodiment, the indicator is a pulserate estimate and the processor further comprises a window configured toinclude the pulse rate estimate, and a plurality of frequencies selectedfrom within the window. In another embodiment, the characteristiccomprises a plurality of signal remainders corresponding to the basisfunctions and a plurality of magnitudes of the signal remainders. Inthat embodiment, the optimization comprises a minima of the magnitudes.In a further embodiment, the characteristic comprises a plurality ofcomponents corresponding to the basis functions and a plurality ofmagnitudes of the components. In this embodiment, the optimizationcomprises a maxima of the magnitudes.

An aspect of a signal component processing method comprises the steps ofreceiving a sensor signal, calculating an estimated pulse rate,determining an optimization of the sensor signal proximate the estimatedpulse rate, defining a frequency corresponding to the optimization, andoutputting a physiological measurement responsive to a component of thesensor signal at the frequency. In one embodiment the determining stepcomprises the substeps of transforming the sensor signal to a frequencyspectrum encompassing the estimated pulse rate and detecting an extremaof the spectrum indicative of the frequency. The transforming step maycomprise the substeps of defining a window including the estimated pulserate, defining a plurality of selected frequencies within the window,canceling the selected frequencies, individually, from the sensor signalto generate a plurality of remainder signals and calculating a pluralityof magnitudes of the remainder signals. The detecting step may comprisethe substep of locating a minima of the magnitudes.

In another embodiment, the outputting step comprises the substeps ofinputting a red (RD) portion and an infrared (IR) portion of the sensorsignal, deriving a RD component of the RD portion and an IR component ofthe IR portion corresponding to the frequency and computing an oxygensaturation based upon a magnitude ratio of the RD component and the IRcomponent. The deriving step may comprise the substeps of generating asinusoidal waveform at the frequency and selecting the RD component andthe IR component utilizing the waveform. The selecting step may comprisethe substep of calculating the inner product between the waveform andthe RD portion and the inner product between the waveform and the IRportion. The selecting step may comprise the substeps of canceling thewaveform from the RD portion and the IR portion, leaving a RD remainderand an IR remainder, and subtracting the RD remainder from the RDportion and the IR remainder from the IR portion.

A further aspect of a signal component processor comprises a firstcalculator means for deriving an optimization frequency from a pulserate estimate input and a sensor signal, and a second calculator meansfor deriving a physiological measurement responsive to a sensor signalcomponent at the frequency. In one embodiment, the first calculatormeans comprises a signal component transform means for determining aplurality of signal values corresponding to a plurality of selectedfrequencies within a window including the pulse rate estimate, and adetection means for determining a particular one of the selectedfrequencies corresponding to an optimization of the sensor signal. Thesecond calculator means may comprise a waveform means for generating asinusoidal signal at the frequency, a frequency selection means fordetermining a component of the sensor signal from the sinusoidal signaland a calculator means for deriving a ratio responsive to the component.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-C are graphical representations of a pulse oximetry sensorsignal;

FIG. 1A is a typical plethysmograph illustrating blood volume versustime;

FIG. 1B is a pulse oximetry sensor signal illustrating detected lightintensity versus time;

FIG. 1C is a pulse oximetry sensor signal spectrum illustrating bothhigh signal quality and low signal quality conditions;

FIGS. 2-3 are magnitude versus frequency graphs for a pulse oximetrysensor signal illustrating an example of signal component processing;

FIG. 2 illustrates a frequency window around an estimated pulse rate;and

FIG. 3 illustrates an associated signal component transform;

FIGS. 4-7 are functional block diagrams of one embodiment of a signalcomponent processor;

FIG. 4 is a top-level functional block diagram of a signal componentprocessor;

FIG. 5 is a functional block diagram of a frequency calculator;

FIG. 6 is a functional block diagram of a saturation calculator; and

FIG. 7 is a functional block diagram of one embodiment of a frequencyselection;

FIGS. 8A-B are flowcharts of an iterative embodiment of a frequencycalculator; and

FIGS. 9-11 are functional block diagrams of another embodiment of asignal component processor;

FIG. 9 is a top-level functional block diagram of a signal componentprocessor;

FIG. 10 is a functional block diagram of an index calculator; and

FIG. 11 is a functional block diagram of a measurement calculator.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIGS. 2 and 3 provide graphical illustration examples of signalcomponent processing. Advantageously, signal component processingprovides a direct method for the calculation of saturation based onpulse rate. For example, it is not necessary to compute a frequencytransform, such as an FFT, which derives an entire frequency spectrum.Rather, signal component processing singles out specific signalcomponents, as described in more detail below. Further, signal componentprocessing advantageously provides a method of refinement for thecalculation of saturation based on pulse rate.

FIG. 2 illustrates high and low signal quality sensor signal spectrums150, 160 as described with respect to FIG. 1C, above. A frequency window220 is created, including a pulse rate estimate PR 210. A pulse rateestimate can be calculated as disclosed in U.S. Pat. No. 6,002,952,entitled “Signal Processing Apparatus and Method,” assigned to theassignee of the present invention and incorporated by reference herein.A search is conducted within this window 220 for a component frequencyf₀ at an optimization. In particular, selected frequencies 230, whichinclude PR, are defined within the window 220. The components of asignal s(t) at each of these frequencies 230 are then examined for anoptimization indicative of an extrema of energy, power or other signalcharacteristic. In an alternative embodiment, the components of thesignal s(t) are examined for an optimization over a continuous range offrequencies within the window 220.

FIG. 3 illustrates an expanded portion of the graph described withrespect to FIG. 2, above. Superimposed on the high signal quality 150and low signal quality 160 spectrums is a signal component transform310. In one embodiment, a signal component transform 310 is indicativeof sensor signal energy and is calculated at selected signal frequencies230 within the window 220. A signal component transform 310 has anextrema 320 that indicates, in this embodiment, energy optimization at aparticular one 330 of the selected frequencies 230. The extrema 320 canbe, for example, a maxima, minima or inflection point. In the embodimentillustrated, each point of the transform 310 is the magnitude of thesignal remaining after canceling a sensor signal component at one of theselected frequencies. The extrema 320 is a minima, which indicates thatcanceling the corresponding frequency 330 removes the most signalenergy. In an alternative embodiment, not illustrated, the transform 310is calculated as the magnitude of signal components at each of theselected frequencies 230. In that embodiment, the extrema is a maxima,which indicates the largest energy signal at the correspondingfrequency. The result of a signal component transform 310 isidentification of a frequency f₀ 330 determined from the frequency of asignal component transform extrema 320. Frequency f₀ 330 is then used tocalculate an oxygen saturation. A signal component transform andcorresponding oxygen saturation calculations are described in additionaldetail with respect to FIGS. 4-8, below. Although signal componentprocessing is described above with respect to identifying a particularfrequency within a window including a pulse rate estimate PR, a similarprocedure could be performed on 2PR, 3PR etc. resulting in theidentification of multiple frequencies f₀₁, f₀₂, etc., which could beused for the calculation of oxygen saturation as well.

Advantageously, a signal component transform 310 is calculated over anyset of selected frequencies, unrestricted by the number or spacing ofthese frequencies. In this manner, a signal component transform 310differs from a FFT or other standard frequency transforms. For example,a FFT is limited to N evenly-distributed frequencies spaced at aresolution of f_(s)/N, where N is the number of signal samples and f_(s)is the sampling frequency. That is, for a FFT, a relatively highsampling rate or a relatively large record length or both are needed toachieve a relatively high resolution in frequency. Signal componentprocessing, as described herein, is not so limited. Further, a signalcomponent transform 310 is advantageously calculated only over a rangeof frequencies of interest. A FFT or similar frequency transformationmay be computationally more burdensome than signal component processing,in part because such a transform is computed over all frequencies withina range determined by the sampling frequency, f_(s).

FIGS. 4-7 illustrate one embodiment of a signal component processor.FIG. 4 is a top-level functional block diagram of a signal componentprocessor 400. The signal component processor 400 has a frequencycalculator 410 and a saturation calculator 460. The frequency calculator410 has an IR signal input 402, a pulse rate estimate signal PR input408 and a component frequency f₀ output 412. The frequency calculator410 performs a signal component transform based upon the PR input 408and determines the f₀ output 412, as described with respect to FIGS.2-3, above. The frequency calculator 410 is described in further detailwith respect to FIG. 5, below.

In an alternative embodiment, the frequency calculator 410 determines f₀412 based upon a RD signal input substituted for, or in addition to, theIR signal input 402. Similarly, one of ordinary skill in the art willrecognize that f₀ can be determined by the frequency calculator 410based upon one or more inputs responsive to a variety of sensorwavelengths.

The saturation calculator 460 has an IR signal input 402, a RD signalinput 404, a component frequency f₀ input 412 and an oxygen saturationoutput, SAT_(f) ₀ 462. The saturation calculator 460 determines valuesof the IR signal input 402 and the RD signal input 404 at the componentfrequency f₀ 412 and computes a ratio of those values to determineSAT_(f) ₀ 462, as described with respect to FIG. 6, below. The IR signalinput 402 and RD signal input 404 can be expressed as: $\begin{matrix}{{{IR} = \begin{bmatrix}{IR}_{0} \\\vdots \\{IR}_{N - 1}\end{bmatrix}};\quad{{RD} = \begin{bmatrix}{RD}_{0} \\\vdots \\{RD}_{N - 1}\end{bmatrix}}} & (1)\end{matrix}$

where N is the number of samples of each signal input.

FIG. 5 shows one embodiment of the frequency calculator 410. In thisparticular embodiment, the frequency calculator functions are windowgeneration 520, frequency cancellation 540, magnitude calculation 560and minima determination 580. Window generation 520, frequencycancellation 540 and magnitude calculation 560 combine to create asignal component transform 310 (FIG. 3), as described with respect toFIG. 3, above. Minima determination 580 locates the signal componenttransform extrema 320 (FIG. 3), which identifies f₀ 412, also describedwith respect to FIG. 3, above.

As shown in FIG. 5, window generation 520 has a PR input 408 and definesa window 220 (FIG. 3) about PR 210 (FIG. 3) including a set of selectedfrequencies 230 (FIG. 3) $\begin{matrix}{\left\{ {{f_{m};{m = 0}},{{\ldots\quad M} - 1}} \right\};\quad{f = \begin{bmatrix}f_{0} \\\vdots \\f_{M - 1}\end{bmatrix}}} & (2)\end{matrix}$

where M is the number of selected frequencies 230 (FIG. 3) within thewindow 220 (FIG. 3). Window generation 520 has a sinusoidal outputX_(f), Y_(f) 522, which is a set of sinusoidal waveforms x_(n,f),y_(n,f) each corresponding to one of the set of selected frequencies 230(FIG. 3). Specifically $\begin{matrix}{{X_{f} = \begin{bmatrix}x_{0,f} \\\vdots \\x_{{N - 1},f}\end{bmatrix}};\quad{Y_{f} = \begin{bmatrix}y_{0,\quad f} \\\vdots \\y_{{N\quad - \quad 1},\quad f}\end{bmatrix}}} & \left( {3a} \right) \\{{x_{n,f} = {\sin\left( {2\quad\pi\quad{fn}} \right)}};\quad{y_{n,f} = {\cos\left( {2\quad\pi\quad{fn}} \right)}}} & \left( {3b} \right)\end{matrix}$

Also shown in FIG. 5, the frequency cancellation 540 has IR 402 andX_(f), Y_(f) 522 inputs and a remainder output R_(f) 542, which is a setof remainder signals r_(n,f) each corresponding to one of the sinusoidalwaveforms x_(n,f), y_(n,f). For each selected frequency f 230 (FIG. 3),frequency cancellation 540 cancels that frequency component from theinput signal IR 402 to generate a remainder signal r_(n,f). Inparticular, frequency cancellation 540 generates a remainder R_(f) 542$\begin{matrix}{R_{f} = \begin{bmatrix}r_{0,\quad f} \\\vdots \\r_{{N\quad - \quad 1},\quad f}\end{bmatrix}} & \left( {4a} \right) \\{R_{f} = {{IR} - {\frac{{IR} \cdot X_{f}}{{X_{f}}^{2}}X_{f}} - {\frac{{IR} \cdot Y_{f}}{{Y_{f}}^{2}}Y_{f}}}} & \left( {4b} \right)\end{matrix}$

Additionally, as shown in FIG. 5, the magnitude calculation 560 has aremainder input R_(f) 542 and generates a magnitude output W_(f) 562,where $\begin{matrix}{W_{f} = {{R_{f}} = \sqrt{\sum\limits_{n = 0}^{N - 1}r_{n,f}^{2}}}} & (5)\end{matrix}$

Further shown in FIG. 5, the minima determination 580 has the magnitudevalues W_(f) 562 as inputs and generates a component frequency f₀output. Frequency f₀ is the particular frequency associated with theminimum magnitude valueW_(f) ₀ =min {W_(f)}  (6)

FIG. 6 shows that the saturation calculator 460 functions are sinusoidgeneration 610, frequency selection 620, 640 and ratio calculation 670.Sinusoid generation has a component frequency f₀ input 208 and asinusoidal waveform X_(f) ₀ , Y_(f) ₀ output 612, which has a frequencyof f₀. Frequency selection 620, 640 has a sensor signal input, which iseither an IR signal 202 or a RD signal 204 and a sinusoid waveform X_(f)₀ , Y_(f) ₀ input 612. Frequency selection 620, 640 provides magnitudeoutputs z_(IR,f) ₀ 622 and z_(RD,f) ₀ 642 which are the frequencycomponents of the IR 202 and RD 204 sensor signals at the f₀ frequency.Specifically, from equation 3(a) $\begin{matrix}{{X_{f_{o}} = \begin{bmatrix}x_{0,f_{o}} \\\vdots \\x_{{N - 1},f_{o}}\end{bmatrix}};\quad{Y_{f_{o}} = \begin{bmatrix}y_{0,\quad f_{o}} \\\vdots \\y_{{N\quad - \quad 1},\quad f_{o}}\end{bmatrix}}} & (7)\end{matrix}$

Then, referring to equation 1 $\begin{matrix}\begin{matrix}{z_{{IR},f_{o}} = {{{\frac{{IR} \cdot X_{f_{o}}}{{X_{f_{o}}}^{2}}X_{\quad f_{\quad o}}} + {\frac{{IR} \cdot \quad Y_{\quad f_{\quad o}}}{\quad{\quad Y_{\quad f_{\quad o}}}^{2}}Y_{f_{o}}}}}} \\{= \sqrt{\frac{\left( {{IR} \cdot X_{f_{o}}} \right)^{2}}{{X_{f_{o}}}^{2}} + \frac{\left( {{IR} \cdot Y_{f_{o}}} \right)^{2}}{{Y_{f_{o}}}^{2}}}}\end{matrix} & \left( {8a} \right) \\\begin{matrix}{z_{{RD},f_{o}} = {{{\frac{{RD} \cdot X_{f_{o}}}{{X_{f_{o}}}^{2}}X_{\quad f_{\quad o}}} + {\frac{{RD} \cdot \quad Y_{\quad f_{\quad o}}}{\quad{\quad Y_{\quad f_{o}}}^{2}}Y_{f_{o}}}}}} \\{= \sqrt{\frac{\left( {{RD} \cdot X_{f_{o}}} \right)^{2}}{{X_{f_{o}}}^{2}} + \frac{\left( {{RD} \cdot Y_{f_{o}}} \right)^{2}}{{Y_{f_{o}}}^{2}}}}\end{matrix} & \left( {8b} \right)\end{matrix}$

For simplicity of illustration, EQS. 8a-b assume that the cross-productof X_(f) ₀ and Y_(f) ₀ is zero, although generally this is not the case.The ratio calculation and mapping 670 has z_(IR,f) ₀ 622 and x_(RD,f) ₀642 as inputs and provides SAT_(f) ₀ 262 as an output. That isSAT _(f) ₀ =g{z _(RD,f) ₀ /z _(IR,f) ₀ }  (9)

where g is a mapping of the red-over-IR ratio to oxygen saturation,which may be an empirically derived lookup table, for example.

FIG. 7 illustrates an alternative embodiment of frequency selection 620(FIG. 6), as described above. In this embodiment, frequency cancellation540 and magnitude calculation 560, as described with respect to FIG. 5,can also be used, advantageously, to perform frequency selection.Specifically, frequency cancellation 540 has IR 202 and X_(f) ₀ ,Y_(f) ₀612 as inputs and generates a remainder signal R_(f) ₀ 712 as an output,where $\begin{matrix}{R_{f_{o}} = {{IR} - {\frac{{IR} \cdot X_{f_{o}}}{{X_{f_{o}}}^{2}}X_{\quad f_{\quad o}}} - {\frac{{IR} \cdot \quad Y_{\quad f_{\quad o}}}{\quad{\quad Y_{\quad f_{\quad o}}}^{2}}Y_{f_{o}}}}} & (10)\end{matrix}$

The remainder R_(f) ₀ 712 is subtracted 720 from IR 202 to yield$\begin{matrix}\begin{matrix}{Z_{f_{o}} = {{IR} - \left\lbrack {{IR} - {\frac{{IR} \cdot X_{f_{o}}}{{X_{f_{o}}}^{2}}X_{\quad f_{o}}} - {\frac{{IR} \cdot \quad Y_{\quad f_{\quad o}}}{\quad{\quad Y_{\quad f_{\quad o}}}^{2}}Y_{f_{o}}}} \right\rbrack}} \\{= {{\frac{{IR} \cdot X_{f_{o}}}{{X_{f_{o}}}^{2}}X_{\quad f_{\quad o}}} + {\frac{{IR} \cdot \quad Y_{\quad f_{\quad o}}}{\quad{\quad Y_{\quad f_{\quad o}}}^{2}}Y_{f_{o}}}}}\end{matrix} & (11)\end{matrix}$

where Z_(f) ₀ 722 is the component of IR 202 at the f₀ frequency. Themagnitude calculation 560 has Z_(f) ₀ 722 as an input and calculates$\begin{matrix}\begin{matrix}{{Z_{f_{o}}} = {{{\frac{{IR} \cdot X_{f_{o}}}{{X_{f_{o}}}^{2}}X_{\quad f_{\quad o}}} + {\frac{{IR} \cdot \quad Y_{\quad f_{\quad o}}}{\quad{\quad Y_{\quad f_{\quad o}}}^{2}}Y_{f_{o}}}}}} \\{= \sqrt{\frac{\left( {{IR} \cdot X_{f_{o}}} \right)^{2}}{{X_{f_{o}}}^{2}} + \frac{\left( {{IR} \cdot \quad Y_{\quad f_{\quad o}}} \right)^{2}}{\quad{\quad Y_{\quad f_{o}}}^{2}}}}\end{matrix} & (12)\end{matrix}$

which is equivalent to equation 8a, above.

FIGS. 8A-B illustrate an iterative embodiment of the frequencycalculator 410 (FIG. 4) described above. An iterative frequencycalculator 410 has an initialization 810, a signal component transform820, an extrema detection 850, a resolution decision 860 and aresolution refinement 880 and provides a component frequency f₀ 870.Initialization 810 defines a window around the pulse rate estimate PRand a frequency resolution within that window.

As shown in FIG. 8A, a signal component transform 820 has an initialfrequency selection 822, a frequency cancellation 824 and an magnitudecalculation 828. A decision block 830 determines if the magnitudecalculation 828 has been performed at each frequency within the window.If not, the loop of frequency cancellation 824 and magnitude calculation828 is repeated for another selected frequency in the window. Thefrequency cancellation 824 removes a frequency component from the IRsensor signal, as described with respect to FIG. 5, above. The magnitudecalculation 828 determines the magnitude of the remainder signal, alsodescribed with respect to FIG. 5, above. If the decision block 830determines that the remainder signal magnitudes have been calculated ateach of the selected frequencies, then the signal component transformloop 820 is exited to the steps described with respect to FIG. 8B.

As shown in FIG. 8B, the extrema detector 850 finds a minima of a signalcomponent transform 820 and a resolution decision block 860 determinesif the final frequency resolution of a signal component transform isachieved. If not, resolution refinement 880 is performed. If the finalresolution is achieved, the component frequency output f₀ is equated tothe frequency of the minima 870, i.e. a signal component transformminima determined by the extrema detector 850.

Further shown in FIG. 8B, the resolution refinement 880 has a setfrequency estimate 882, a window decrease 884 and a frequency resolutionincrease 888. Specifically, the frequency estimate 882 is set to asignal component transform minima, as determined by the extrema detector850. The window decrease 884 defines a new and narrower window aroundthe frequency estimate, and the frequency resolution increase 888reduces the spacing of the selected frequencies within that window priorto the next iteration of a signal component transform 820. In thismanner, a signal component transform 820 and the resulting frequencyestimate are refined to a higher resolution with each iteration ofsignal component transform 820, extrema detection 850, and resolutionrefinement 880.

In a particular embodiment, the component calculation requires threeiterations. A frequency resolution of 4 beats per minute or 4 BPM isused initially and a window of five or seven selected frequencies,including that of the initial pulse rate estimate PR, is defined. Thatis, a window of either 16 BPM or 24 BPM centered on PR is defined, and asignal component transform is computed for a set of 5 or 7 selectedfrequencies evenly spaced at 4 BPM. The result is a frequency estimatef₁. Next, the frequency resolution is reduced from 4 BPM to 2 BPM and a4 BPM window centered on f₁ is defined with three selected frequencies,i.e. f₁−2 BPM, f₁, and f₁+2 BPM. The result is a higher resolutionfrequency estimate f₂. On the final iteration, the frequency resolutionis reduced to 1BPM and a 2 BPM window centered on f₂ is defined withthree selected frequencies, i.e. f₂−1 BPM, f₂, and f₂+1 BPM. The finalresult is the component frequency f₀ determined by a signal componenttransform to within a 1BPM resolution. This component frequency f₀ isthen used to calculate the oxygen saturation, SAT_(f0), as describedabove.

The signal component processor has been described above with respect topulse oximetry and oxygen saturation measurements based upon a frequencycomponent that optimizes signal energy. The signal component processor,however, is applicable to other physiological measurements, such asblood glucose, carboxy-hemoglobin, respiration rate and blood pressureto name a few. Further, the signal component processor is generallyapplicable to identifying, selecting and processing any basis functionsignal components, of which single frequency components are oneembodiment, as described in further detail with respect to FIG. 9,below.

FIGS. 9-11 illustrate another embodiment of a signal component processor900. As shown in FIG. 9, the processor 900 has an index calculator 910and a measurement calculator 960. The index calculator has a sensorsignal input S 902 and outputs a basis function index κ₀ 912, asdescribed with respect to FIG. 10, below. The measurement calculator 960inputs the basis function index κ₀ 912 and outputs a physiologicalmeasurement U_(κ) ₀ 962, as described with respect to FIG. 11, below.The processor 900 also has a basis function indicator 980, which isresponsive to the sensor signal input S 902 and provides a parameter ε982 that indicates a set of basis functions to be utilized by the indexcalculator 910, as described with respect to FIG. 10, below.

As shown in FIG. 10, the functions of the index calculator 910 are basissubset generation 1020, component cancellation 1040 and optimizationcalculation 1060. The basis subset generation 1020 outputs a subsetΦ_(κ) 1022 of basis function waveforms corresponding to a set ofselected basis function indices κ. The basis functions can be anycomplete set of functions such that $\begin{matrix}{S = {\sum\limits_{\kappa}^{\quad}\quad{a_{\kappa}\Phi_{\kappa}}}} & (13)\end{matrix}$

For simplicity of illustration purposes, these basis functions areassumed to be orthogonal<{right arrow over (Φ)}_(γ), {right arrow over (Φ)}_(η)>=0; γ≢η  (14)

where < > denotes an inner product. As sucha _(κ) =<S, Φ _(κ)>/<Φ_(κ), Φ_(κ)>  (15)S_(κ)=a_(κ)Φ_(κ)  (16)

In general, the basis functions may be non-orthogonal. The subset ofbasis functions generated is determined by an input parameter ε 982. Inthe embodiment described with respect to FIG. 5, above, the basisfunctions are sinusoids, the indices are the sinusoid frequencies andthe input parameter ε 982 is a pulse rate estimate that determines afrequency window.

As shown in FIG. 10, the component cancellation 1040 generates aremainder output R_(κ) 1042, which is a set of remainder signalscorresponding to the subset of basis function waveforms Φ_(κ) 1022. Foreach basis function waveform generated, component cancellation removesthe corresponding basis function component from the sensor signal S 902to generate a remainder signal. In an alternative embodiment, componentcancellation 1040 is replaced with a component selection that generatesa corresponding basis function component of the sensor signal S 902 foreach basis function generated. The optimization calculation 1060generates a particular index κ₀ 912 associated with an optimization ofthe remainders R_(κ) 1042 or, alternatively, an optimization of theselected basis function signal components.

As shown in FIG. 11, the functions of the measurement calculator 960 arebasis function generation 1120, component selection 1140, andphysiological measurement calculation 1170. The component selection 1140inputs the sensor signal S 902 and a particular basis function waveformΦ_(κ) ₀ 1122 and outputs a sensor signal component S_(κ) ₀ 1142. Thephysiological measurement 1170 inputs the sensor signal component S_(κ)₀ 1142 and outputs the physiological measurement U_(κ) ₀ 962, which isresponsive to the sensor signal component S_(κ) ₀ 1142. In theembodiment described with respect to FIG. 6, above, the basis functionsΦ_(κ) are sinusoids and the index κ₀ is a particular sinusoid frequency.The basis function generation 1120 creates sine and cosine waveforms atthis frequency. The component selection 1140 selects correspondingfrequency components of the sensor signal portions, RD and IR. Also, thephysiological measurement 1170 computes an oxygen saturation based upona magnitude ratio of these RD and IR frequency components.

The signal component processor has been disclosed in detail inconnection with various embodiments. These embodiments are disclosed byway of examples only and are not to limit the scope of the claims thatfollow. One of ordinary skill in the art will appreciate many variationsand modifications.

1. A sine saturation transform method for filtering a physiologicalsignal to improve a calculation of a physiological parameter, the sinesaturation transform method comprising: obtaining a signal comprisingpulse rate information; determining at least one sinusoidal wave thatupon combination produces a predetermined response; filtering the signalbased on a frequency of the at least one sinusoidal wave; andcalculating a physiological parameter from the filtered signal.
 2. Themethod of claim 1, comprising generating one or more sinusoidal waves.3. The method of claim 2, comprising individually canceling thegenerated sinusoidal waves from the signal.
 4. The method of claim 1,comprising determining a frequency corresponding to the sinusoidal wavewhich produces an extrema.
 5. The method of claim 1, wherein thephysiological parameter comprises one or more of blood oxygensaturation, blood glucose, carboxyhemoglobin, and blood pressure.
 6. Themethod of claim 1, comprising segmenting the signal into at least onepulse rate cycle.